---
title: "Attention Backend"
metatags:
    description: "SGLang attention backend guide: FlashInfer, FA3, FA4, Triton, FlashMLA, TRTLLM MLA, hybrid attention. Support matrix for MHA and MLA models."
---
SGLang supports a large variety of attention backends. Each of them has different pros and cons.
You can test them according to your needs.

<Warning>
Selecting an optimal attention backend is crucial for maximizing your performance. Different backends excel in various scenarios, so choose based on your model, hardware, and use case. Not all backends are supported on all platforms and model architectures.

If you don't specify `--attention-backend`, SGLang makes a best effort to automatically select the most performant backend based on your hardware and model architecture.
</Warning>

## Support Matrix

The support matrix is split into two parts: MHA (standard attention) and MLA (multi-head latent attention). For an explanation of the key differences between MHA and MLA, please see the [SGLang documentation on DeepSeek MLA](/cookbook/autoregressive/DeepSeek/DeepSeek-V3#4-2-4-mla-optimizations) and the original [DeepSeek MLA paper](https://arxiv.org/pdf/2405.04434).

### MHA Backends

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "13%"}} />
    <col style={{width: "13%"}} />
    <col style={{width: "13%"}} />
    <col style={{width: "13%"}} />
    <col style={{width: "12%"}} />
    <col style={{width: "12%"}} />
    <col style={{width: "12%"}} />
    <col style={{width: "12%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**Backend**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>**Page Size > 1 (native)**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**FP8 KV Cache**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>**FP4 KV Cache**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**Spec topk=1**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>**Spec topk>1**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**Sliding Window**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>**MultiModal**</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FlashInfer**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FA3 (FlashAttention 3)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FA4 (FlashAttention 4)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>128</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Triton**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Torch Native (SDPA)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FlexAttention (PyTorch)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**TRTLLM MHA**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>16, 32 or 64</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Dual Chunk FlashAttention**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**HPC-Ops**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>64</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**AITER (ROCm)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Wave (ROCm)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Ascend (NPU)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Intel XPU**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Intel AMX (CPU)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
  </tbody>
</table>

### MLA Backends

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "15%"}} />
    <col style={{width: "15%"}} />
    <col style={{width: "14%"}} />
    <col style={{width: "14%"}} />
    <col style={{width: "14%"}} />
    <col style={{width: "14%"}} />
    <col style={{width: "14%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**Backend**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>**Native Page Sizes**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**FP8 KV Cache**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>**FP4 KV Cache**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**Chunked Prefix Cache**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>**Spec topk=1**</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>**Spec topk>1**</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FlashInfer MLA**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FlashMLA**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>64</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Cutlass MLA**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>128</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**TRTLLM MLA (Blackwell)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>32 or 64</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**CuteDSL MLA (Blackwell)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>32 or 64</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**TokenSpeed MLA (Blackwell)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>32 or 64</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅ (required)</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FA3 (FlashAttention 3)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>n/a</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⚠️ (page_size=1 only)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Triton**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>n/a</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>⚠️ (page_size=1 only)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**FA4**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>1</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>**Ascend MLA (NPU)**</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>128</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
    </tr>
  </tbody>
</table>

<Note>
Multimodal attention is selected by `--mm-attention-backend`. The "MultiModal" column indicates whether a corresponding multimodal implementation exists for that backend family.
</Note>

<Note>
- DSA is specifically designed for [DeepSeek V3.2](https://lmsys.org/blog/2025-09-29-deepseek-V32/). See the [DSA Attention Backend](#dsa-attention-backend) section and [DeepSeek V3.2 deployment guide](/cookbook/autoregressive/DeepSeek/DeepSeek-V3_2) for details.
</Note>

<Note>
For the KV4 FA4 scenario, FA4 requires using a different --decode-attention-backend to run. Except for trtllm_mha being incompatible with FA4, all other decode backends behave as shown in the table.
</Note>

<Tip>
Speculative decoding topk: `topk` is the number of draft tokens sampled per step from the draft model. `topk = 1` follows classic EAGLE; `topk > 1` explores multiple branches and requires backend support in both draft and verification paths.
</Tip>

<Tip>
Page size controls how many tokens are grouped into a KV cache block. For the prefix cache to take effect, the number of tokens must fill at least one complete page. For example, if your prompt is only 32 tokens and `page_size = 64`, it won't fill a complete page and cannot be matched in the prefix cache (pages cannot be padded). With 65 tokens and `page_size = 64`, only the first page of 64 tokens will be cached and matched; the remaining 1 token is discarded. Use `page_size = 1` for maximum prefix reuse (token-level matching). Note that higher page sizes generally improve attention kernel performance, so prefer `page_size > 1` when prefix cache reuse is not critical.
</Tip>

Many backends that do not natively operate on pages can emulate `page_size > 1` at the wrapper layer by expanding page tables to per-token indices. The "Page Size > 1 (native)" column indicates true in-kernel paging. Some backends require fixed native page sizes and cannot be reduced/emulated differently: TRTLLM MHA (16/32/64), TRTLLM MLA (32/64), CuteDSL MLA (32/64), FlashMLA (64), Cutlass MLA (128), Ascend (128), HPC-Ops (64).

MLA page-size constraints:
- FlashInfer MLA: page_size = 1.
- FlashMLA: page_size = 64.
- Cutlass MLA: page_size = 128.
- TRTLLM MLA: page_size ∈ &#123;32, 64&#125;.
- CuteDSL MLA: page_size ∈ &#123;32, 64&#125; (decode-only; prefill falls back to `trtllm_mla` when unset).
- TokenSpeed MLA: page_size ∈ &#123;32, 64&#125; (Blackwell SM100/SM12x only; requires `--kv-cache-dtype fp8_e4m3`).

### GDN Attention Backends

GDN (Gated Delta Network) is a linear attention mechanism with O(n) complexity, used in hybrid models that alternate GDN linear attention layers with standard full attention layers. GDN is **not** selected via `--attention-backend`; it is automatically activated when the model architecture requires it (e.g., Qwen 3.5, Qwen 3 Next, Jet Nemotron, Jet VLM).

The GDN linear attention layers have their own kernel backends, selected via `--linear-attn-backend` (default: `triton`). You can override the kernel per phase with `--linear-attn-decode-backend` and `--linear-attn-prefill-backend`.

On SM100/SM103 with CUDA 13+, SGLang automatically selects FlashInfer for GDN prefill when the per-phase override is unset, the base linear-attention backend is Triton, recurrent state is BF16, key/value head dimensions are 128, dynamic chunking and page-major KV layout are disabled, and `--chunked-prefill-size` is between 1 and 8192. Radix caching may be disabled or use `no_buffer`, `extra_buffer`, or `extra_buffer_lazy`; the extra-buffer paths use state checkpoints.

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "28%"}} />
    <col style={{width: "16%"}} />
    <col style={{width: "24%"}} />
    <col style={{width: "32%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Backend</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Decode</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Prefill / Extend</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Spec Decoding (Target Verify)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>Triton (CUDA)</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>Triton (AMD/ROCm)</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>Triton (NPU)</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>Triton (CPU)</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>CuTe DSL (CUDA only)</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>❌</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>FlashInfer (CUDA, SM90/SM100/SM103)</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅ linear chain; tree falls back to Triton</td>
    </tr>
  </tbody>
</table>

<Warning>
GDN models are hybrid: the full-attention layers still require a standard `--attention-backend`. Platform constraints for the full-attention backend on hybrid GDN models:
- **Blackwell SM120 (e.g., RTX PRO 6000 Blackwell)**: `triton` or `flashinfer` for prefill/full attention; `trtllm_mha` is supported for `--decode-attention-backend` only.
- **Other Blackwell variants (including SM100 B200/GB200)**: `triton`, `trtllm_mha`, or `fa4` only.
- **NPU (Ascend)**: `ascend` only.
- **AMD (ROCm)**: `triton` recommended.
- **Other CUDA (Hopper, Ampere, etc.)**: auto-selection works; no special constraints.
</Warning>

### DSA Attention Backend

DSA (DeepSeek Sparse Attention) is a native sparse attention mechanism used by [DeepSeek V3.2](https://lmsys.org/blog/2025-09-29-deepseek-V32/). It is activated automatically when the model architecture requires it and is selected via `--attention-backend dsa` (deprecated alias: `nsa`).

Internally, the DSA backend dispatches to different sub-backends for prefill and decode phases. You can override these with `--dsa-prefill-backend` and `--dsa-decode-backend`:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "26%"}} />
    <col style={{width: "16%"}} />
    <col style={{width: "16%"}} />
    <col style={{width: "42%"}} />
  </colgroup>
  <thead>
    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Sub-backend</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Prefill</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Decode</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Notes</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>flashmla_sparse</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Default prefill on Hopper and Blackwell (BF16)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>flashmla_sparse_q8</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Native FP8 (q8×kv8) sparse prefill on Hopper (SM90); requires <code>--kv-cache-dtype fp8_e4m3</code></td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>flashmla_kv</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Default for FP8 on Hopper (prefill + decode)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>flashmla_auto</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>❌</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Picks flashmla_sparse or flashmla_kv by KV cache dtype</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>fa3</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Default decode on Hopper (BF16)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>trtllm</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Default decode on Blackwell (BF16); default for FP8 on Blackwell (prefill + decode)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>tilelang</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Default on AMD (ROCm)</td>
    </tr>
    <tr>
      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>aiter</strong></td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>✅</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>AMD-specific kernel library (requires aiter package)</td>
    </tr>
  </tbody>
</table>

For deployment examples, see the [DeepSeek V3.2 deployment guide](/cookbook/autoregressive/DeepSeek/DeepSeek-V3_2).

### Hybrid attention (different backends for prefill vs decode) (Experimental)

<Warning>
Hybrid attention is an experimental feature.
</Warning>

You can mix-and-match attention backends for prefill and decode. This is useful when one backend excels at prefill and another excels at decode. For the implementation details, please see `python/sglang/srt/layers/attention/hybrid_attn_backend.py`.

```bash Command
# Example: Prefill with FA4, Decode with TRTLLM MLA (Blackwell)
python3 -m sglang.launch_server \
  --model-path nvidia/DeepSeek-R1-FP4 \
  --tp 8 \
  --attention-backend trtllm_mla \
  --moe-runner-backend flashinfer_trtllm \
  --quantization modelopt_fp4 \
  --prefill-attention-backend fa4
```

#### Speculative decoding with hybrid attention

Hybrid attention also works with speculative decoding. The backend used for draft decoding and target verification depends on `--speculative-attention-mode`:

- `--speculative-attention-mode decode` (recommended): draft/verify use the decode backend.
- `--speculative-attention-mode prefill` (default): draft/verify use the prefill backend.

Constraints when combining hybrid attention with speculative decoding:

- If any attention backend is `trtllm_mha`, speculative decoding supports only `--speculative-eagle-topk 1`.
- For paged MHA backends with `--page-size > 1` and `--speculative-eagle-topk > 1`, only `flashinfer` is supported.
- CUDA Graph: the decode backend is always captured; the prefill backend is captured only when `--speculative-attention-mode prefill`.


<Tip>
If you set only one of `--prefill-attention-backend` or `--decode-attention-backend`, the unspecified phase inherits `--attention-backend`.
If both are specified and differ, SGLang automatically enables a hybrid wrapper to dispatch to the chosen backend per phase.
</Tip>

## Attention Backend Selection Guide (CUDA)

If the `--attention-backend` argument is not specified, SGLang automatically selects the best backend based on the hardware (CUDA) and model architecture.

### Automatic Selection Logic

**1. MHA Models (e.g., Llama, Qwen)**
- **Hopper (e.g., H100, H200)**: Defaults to `fa3` if using CUDA 12.3+ and the model configuration is supported.
- **Blackwell (e.g., B200)**: Defaults to `trtllm_mha`, unless using speculative decoding with `topk > 1`.
- **Other Architectures (Ampere, Ada, etc.)**: Defaults to `flashinfer` if available; otherwise falls back to `triton`.

**2. MLA Models (e.g., DeepSeek V3)**
- **Hopper**: Defaults to `fa3` (requires CUDA 12.3+).
- **Blackwell**: Defaults to `flashinfer`; `trtllm_mla` is auto-selected for DeepSeek V3 models specifically.
- **Other Architectures**: Defaults to `triton`.


## User Guide

### Launch Command for Different Attention Backends

- FlashInfer (Default for Non-Hopper Machines, e.g., A100, A40)
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend flashinfer
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-V3 \
  --attention-backend flashinfer \
  --trust-remote-code
```

- FlashAttention 3 (Default for Hopper Machines, e.g., H100, H200, H20)
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend fa3
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-V3 \
  --trust-remote-code \
  --attention-backend fa3
```

- Triton
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend triton
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-V3 \
  --attention-backend triton \
  --trust-remote-code
```

- FlashMLA
```bash Command
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend flashmla \
  --trust-remote-code
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend flashmla \
  --kv-cache-dtype fp8_e4m3 \
  --trust-remote-code
```

- TRTLLM MLA (Optimized for Blackwell Architecture, e.g., B200)
```bash Command
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend trtllm_mla \
  --trust-remote-code
```

- TRTLLM MLA with FP8 KV Cache (Higher concurrency, lower memory footprint)
```bash Command
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend trtllm_mla \
  --kv-cache-dtype fp8_e4m3 \
  --trust-remote-code
```

- TRTLLM MHA (Optimized for Blackwell Architecture, e.g., B200)
```bash Command
python3 -m sglang.launch_server \
  --tp 4 \
  --model Qwen/Qwen3.5-35B-A3B-FP8 \
  --attention-backend trtllm_mha \
  --trust-remote-code
```

- TRTLLM MHA (XQA backend) (Optimized for SM90 and SM120, e.g., H20, H200, 5090)
  Note that TRTLLM XQA backend only works well for pagesize 64.
```bash Command
python3 -m sglang.launch_server \
  --tp 4 \
  --model Qwen/Qwen3.5-35B-A3B-FP8 \
  --decode-attention-backend trtllm_mha \
  --trust-remote-code
```

- HPC-Ops (MHA kernels from [HPC-Ops](https://github.com/Tencent/hpc-ops) by the Tencent Hunyuan AI Infra team; Hopper (SM90) only, requires installing the `hpc` package from source, page size 64, bf16 or fp8_e4m3 KV cache, head_dim 128, q/kv head group 4 or 8)
```bash Command
python3 -m sglang.launch_server \
  --model Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 \
  --attention-backend hpc_ops \
  --page-size 64 \
  --trust-remote-code

# FP8 models should also set --kv-cache-dtype fp8_e4m3 to run the FP8 attention
# kernels. This enables the fused QKNorm+RoPE+FP8-quant+StoreKV op, which is
# currently wired for Hunyuan V3 and requires per-rank (q_heads, kv_heads) of
# (64, 8) or (8, 1), e.g. --tp 1 or --tp 8 for Hy3.
python3 -m sglang.launch_server \
  --tp 8 \
  --model tencent/Hy3-FP8 \
  --attention-backend hpc_ops \
  --kv-cache-dtype fp8_e4m3 \
  --page-size 64 \
  --trust-remote-code
```

- FlashAttention 4 (MHA & MLA)
```bash Command
# FA4 for both prefill and decode on SM90/SM100
python3 -m sglang.launch_server \
  --model-path Qwen/Qwen3-30B-A3B-Instruct-2507-FP8 \
  --attention-backend fa4 \
  --page-size 128 \
  --trust-remote-code

python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --prefill-attention-backend fa4 \
  --trust-remote-code
```

- Cutlass MLA
```bash Command
python3 -m sglang.launch_server \
  --tp 8 \
  --model deepseek-ai/DeepSeek-R1 \
  --attention-backend cutlass_mla \
  --trust-remote-code
```

- Ascend
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend ascend
```

- Intel XPU
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend intel_xpu
```

- Wave
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend wave
```

- FlexAttention
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend flex_attention
```

- Dual Chunk FlashAttention
```bash Command
python3 -m sglang.launch_server \
  --model Qwen/Qwen2.5-14B-Instruct-1M \
  --attention-backend dual_chunk_flash_attn
```

- Torch Native
```bash Command
python3 -m sglang.launch_server \
  --model meta-llama/Meta-Llama-3.1-8B-Instruct \
  --attention-backend torch_native
```

## Steps to add a new attention backend
To add a new attention backend, you can learn from the existing backends
(`python/sglang/srt/layers/attention/triton_backend.py`, `python/sglang/srt/layers/attention/flashattention_backend.py`)
and follow the steps below.

<Note>
Linear attention kernel backends (GDN, KDA) follow a different pattern. They implement `LinearAttnKernelBase` in `python/sglang/srt/layers/attention/linear/kernels/` and are dispatched by `GDNKernelDispatcher` / `KDAKernelDispatcher` rather than registered via `@register_attention_backend`.
</Note>

1. Run without cuda graph. Support the two forward functions
    - forward_extend
        - Will be used for prefill, prefill with KV cache, and target verification
        - It will be called once per layer
    - forward_decode
        - Will be used for normal decode, and draft decode
        - It will be called once per layer
    - init_forward_metadata
        - Initialize the class and common metadata shared by all layers
        - Call the plan function for optimizations like split_kv
        - It will be called once per forward
2. Run with cuda graph. It has two phases (capture and replay) and you need to implement three functions
    - init_cuda_graph_state
        - It will be called once during life time
        - Create all common shared buffers
    - init_forward_metadata_capture_cuda_graph
        - It will be called before capturing a cuda graph
        - It is similar to init_forward_metadata but write the medatada to some pre-defined buffers
    - init_forward_metadata_replay_cuda_graph
        - It will be called before replaying a cuda graph
        - This function is in the critical path and needs to be fast
